标题：Robust Constrained Recursive Least P-Power Algorithm for Adaptive Filtering
作者：Sun, Jiajun ;Peng, Siyuan ;Liu, Qinglai ;Zhao, Ruijie ;Lin, Zhiping
作者机构：[Sun, Jiajun ;Peng, Siyuan ;Liu, Qinglai ;Lin, Zhiping ] School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; 更多
会议名称：23rd IEEE International Conference on Digital Signal Processing, DSP 2018
会议日期：19 November 2018 through 21 November 2018
来源：International Conference on Digital Signal Processing, DSP
关键词：CRLP; LMP; non-Gaussian noises
摘要：In this paper, we develop a novel constrained adaptive filtering algorithm called constrained recursive least p-power (CRLP) algorithm, which incorporates a set of linear constraints into the least mean p-power error (LMP) criterion to solve a constrained optimization problem directly. Compared with the conventional constrained adaptive filtering algorithms including constrained least mean square (CLMS), constrained recursive least square (CRLS) and constrained least mean p-power (CLMP), CRLP can achieve better performance under non-Gaussian noises. Simulation results are presented to confirm the superior performance of the new algorithm. © 2018 IEEE.